Human fetal trachea samples collected on Apr3. v3 chemistry.

library(Seurat)
library(dplyr)
ZipF<-list.files(path=".",pattern="*.gz",full.names = T,recursive = T)
ZipF
library(plyr)
library(R.utils)
ldply(.data=ZipF, .fun=gunzip)  #This just unzips locally
##### First I manually changed all featurres.tsv to genes.tsv. Otherwise Read10X (Seurat v2) would not recognize.
# Load data
file_10Xdir_Hs<-c("GA21wk_v3","GA23wk_v3")
names(file_10Xdir_Hs)<-c("GA21wk_v3","GA23wk_v3")
Hs_Apr3_v3.data <- Read10X(data.dir = file_10Xdir_Hs)
dim(Hs_Apr3_v3.data)
26577 genes for HG38-plus
38892 “cells”/barcodes as filtered by Cell Ranger
Hs_GA2123_Trachea_v3@raw.data@Dim
[1] 22084 38892
head(Hs_GA2123_Trachea_v3@cell.names)
Hs_GA2123_Trachea_v3@data@Dim
[1] 22084  9693
cell_name<-read.table(text=Hs_GA2123_Trachea_v3@cell.names,sep="_",colClasses = "character")
age<-cell_name[,1]
names(age)<-Hs_GA2123_Trachea_v3@cell.names
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = age, col.name = "age")
table(Hs_GA2123_Trachea_v3@meta.data$age)

GA21wk GA23wk 
  7623   2070 
ribo.genes <- grep(pattern = "^RP[SL][[:digit:]]", x = rownames(x = Hs_GA2123_Trachea_v3@data), value = TRUE)
percent.ribo <- Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data[ribo.genes, ])/Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data)
Hs_GA2123_Trachea_v3 <- AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = percent.ribo, col.name = "percent.ribo")
Hs_GA2123_Trachea_v3 <- NormalizeData(object = Hs_GA2123_Trachea_v3)
Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Hs_GA2123_Trachea_v3 <- ScaleData(object = Hs_GA2123_Trachea_v3)
Scaling data matrix

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Hs_GA2123_Trachea_v3 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
Calculating gene means
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Hs_GA2123_Trachea_v3 <- RunPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
Hs_GA2123_Trachea_v3 <- ProjectPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
PCHeatmap(object = Hs_GA2123_Trachea_v3, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

PCElbowPlot(object = Hs_GA2123_Trachea_v3)

n.pcs = 20
res.used <- 0.8
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)

TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="age",pt.size = 0.1)

n.pcs = 20
res.used <- 1.0
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc=T)
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1")

Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="res.1")
GA2123wk_v3.res1.clust.markers <- FindAllMarkers(object = Hs_GA2123_Trachea_v3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
GA2123wk_v3.res1.clust.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
write.table(GA2123wk_v3.res1.clust.markers,"GA2123wk_v3.res1.markers.txt",sep="\t")
Hs_v3_res1_8_18<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(8),ident.2=c(18),only.pos = F)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~04s          
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Hs_v3_res1_8_18
Hs_v3_res1_2over1<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(2),ident.2=c(1),only.pos = T)

   |                                                  | 0 % ~calculating  
   |+                                                 | 2 % ~03s          
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   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03s
Hs_v3_res1_2over1
Hs_v3_res1_21_20<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(21),ident.2=c(20),only.pos = T)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~03s          
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Hs_v3_res1_21_20
n.pcs = 20
res.used <- 1.2
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.2")

n.pcs = 20
res.used <- 1.4
Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.4")

sum(is.na(Hs_GA2123_Trachea_v3@meta.data$doublet_score))
[1] 0
Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="age")
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA21wk")

VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("SOX10","PHOX2A", "PHOX2B","CHGA","ASCL1","RET"), nCol = 1,group.by="res.1.4",point.size.use=0.3)
    DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("EPCAM","TUBB3","SNAP25","ASCL1","CHGA","PHOX2A","PHOX2B","PLP1","MPZ"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 35,cex.row=25,cells.use = Hs_GA2123_Trachea_v3@cell.names[Hs_GA2123_Trachea_v3@meta.data$res.1.4 %in% c(10)]
  )
subset the Non-EPCAM cells:
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_nonEpcam<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(0:7,9:13,15,16,18,20:24))
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.1.4)

  0   1  10  11  12  13  15  16  18   2  20  21  22  23  24   3   4   5   6   7   9 
937 862 405 401 370 351 316 290 248 825 189 121  90  88  65 550 481 476 461 447 414 
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.2'] <- 'orig.1.2'
Hs_GA2123_Trachea_v3_nonEpcam <- ScaleData(object = Hs_GA2123_Trachea_v3_nonEpcam)
Scaling data matrix

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  |                                                                                                                                              
  |==================================================================================================================================      |  96%
  |                                                                                                                                              
  |========================================================================================================================================| 100%
Hs_GA2123_Trachea_v3_nonEpcam <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_nonEpcam, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

run PCA on the set of genes
Hs_GA2123_Trachea_v3_nonEpcam <- RunPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_nonEpcam)
Hs_GA2123_Trachea_v3_nonEpcam <- ProjectPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = F)
PCElbowPlot(object = Hs_GA2123_Trachea_v3_nonEpcam)

PCHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, pc.use = 1:20, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

n.pcs = 20
res.used <- 0.8
Hs_GA2123_Trachea_v3_nonEpcam <- FindClusters(object = Hs_GA2123_Trachea_v3_nonEpcam, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.Clustering parameters for resolution 0.8 exactly match those of already computed. 
  To force recalculation, set force.recalc to TRUE.
Hs_GA2123_Trachea_v3_nonEpcam <- RunTSNE(object = Hs_GA2123_Trachea_v3_nonEpcam, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3_nonEpcam, do.label = T,group.by="res.0.8")

table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$orig.1.4,Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.0.8)
    
       0   1  10  11  12  13  14  15  16  17   2   3   4   5   6   7   8   9
  0  917  10   0   0   0   1   1   0   0   0   0   4   4   0   0   0   0   0
  1   13 739  65  18   5  10   0   0   0   0   0  11   0   0   0   1   0   0
  10   2   0   0   1   0   0   0   0   0   0   0   0   1   0   0   0   0 401
  11   0   0   0   1   1   0   0   1   0   0 396   0   0   0   1   1   0   0
  12   0   9   0 313  47   0   0   0   0   0   0   0   0   0   0   1   0   0
  13  11   5   1   0   3   0   0   0   0   0   0   0 329   1   0   1   0   0
  15   0  22 274   0   3   0   0   0   0   0   0  16   0   0   0   1   0   0
  16   0   9   5   4 256   0   1   0   0   0   0   0   0   0  13   0   2   0
  18   1  17   1   0   0 227   0   1   0   0   0   0   0   0   0   0   0   1
  2   15  37  30   0   0   0   0   0   0   0   0 743   0   0   0   0   0   0
  20   0   0   0   0   0   0   0   0   0   0   0   0   4 184   0   0   1   0
  21   5   0   0   0   0   0 115   0   0   0   0   0   0   0   0   1   0   0
  22   0   0   0   0   0   0   0   1  85   0   0   0   0   0   4   0   0   0
  23   0   0   0   0   0   0   0  88   0   0   0   0   0   0   0   0   0   0
  24   0   0   0   0   0   0   0   0   0  65   0   0   0   0   0   0   0   0
  3    0   0   0   0   0   0   0   0   1   0   0   0   0   0 542   7   0   0
  4    5   3   1   0   0   0   0   0   0   0   0   4   0   3   0 463   1   1
  5    1   4   1   1   2   1   0   3   5   0   0   1   1   0   6   1 449   0
  6    0   0   0   0   0   0   0   0   0   0   0   0   4 457   0   0   0   0
  7   19   0   0   0   0   0   0   0   0   0   0   0 414  12   0   1   1   0
  9    0   0   0   0   0   0   0   0   0   0 414   0   0   0   0   0   0   0
library(ggalluvial)
ggplot(data=Hs_GA2123_Trachea_v3_nonEpcam@meta.data,aes(axis1=orig.1.4,axis2=res.0.8))+geom_alluvium(aes(fill=res.0.8))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.0.8"), expand = c(.05, .05))

Now subset the basal, ciliated, and secretory:
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_sub1<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(8,14,19))
table(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.4)
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.2'] <- 'orig.1.2'
Hs_GA2123_Trachea_v3_sub1 <- ScaleData(object = Hs_GA2123_Trachea_v3_sub1)
Scaling data matrix

  |                                                                                                                     
  |                                                                                                               |   0%
  |                                                                                                                     
  |===============================================================================================================| 100%
Hs_GA2123_Trachea_v3_sub1 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_sub1, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

run PCA on the set of genes
Hs_GA2123_Trachea_v3_sub1 <- RunPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_sub1)
Hs_GA2123_Trachea_v3_sub1 <- ProjectPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = F)
PCElbowPlot(object = Hs_GA2123_Trachea_v3_sub1)

PCHeatmap(object = Hs_GA2123_Trachea_v3_sub1, pc.use = 1:12, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

n.pcs = 16
res.used <- 0.8
Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)
TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T)

prop.table(table(Hs_GA2123_Trachea_v3_sub1@meta.data$age,Hs_GA2123_Trachea_v3_sub1@meta.data$res.0.8),1)
        
                  0          1          2          3          4          5          6          7
  GA21wk 0.18484848 0.20303030 0.08333333 0.16818182 0.19090909 0.06363636 0.06060606 0.04545455
  GA23wk 0.18333333 0.10000000 0.31111111 0.10833333 0.05277778 0.12500000 0.06388889 0.05555556
n.pcs = 16
res.used <- 1.2
Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
Build parameters exactly match those of already computed and stored SNN. To force recalculation, set force.recalc to TRUE.
Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

Hs_v3_sub1_res1.2_c8over2_4<-FindMarkers(Hs_GA2123_Trachea_v3_sub1,ident.1=c(8),ident.2 = c(2,4),only.pos = TRUE)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~16s          
   |++                                                | 2 % ~10s          
   |++                                                | 4 % ~07s          
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   |++++                                              | 6 % ~05s          
   |++++                                              | 8 % ~04s          
   |+++++                                             | 9 % ~04s          
   |+++++                                             | 10% ~04s          
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   |++++++++++++++++++++++                            | 42% ~02s          
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   |+++++++++++++++++++++++                           | 45% ~02s          
   |++++++++++++++++++++++++                          | 46% ~01s          
   |++++++++++++++++++++++++                          | 48% ~01s          
   |+++++++++++++++++++++++++                         | 49% ~01s          
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   |++++++++++++++++++++++++++++++++++++++++         | 80% ~00s          
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   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02s
Hs_v3_sub1_res1.2_c8over2_4
library(plyr)
Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.2,from=c("0","1","2","3","4","5","6","7","8"),to=c("Secretory_SMG","Ciliated","Basal_SE","Epcam_ECM","Basal_SE","Myoepithelial","Ciliated_Foxn4","Secretory_SE","Basal_SMG"))

to annotate Hs_GA2123_Trachea_v3
Hs_v3_type_sub1<-Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type
names(Hs_v3_type_sub1)<-Hs_GA2123_Trachea_v3_sub1@cell.names
Hs_GA2123_Trachea_v3@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3@meta.data$res.1,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22"),to=c("Fibroblast","Fibroblast","Fibroblast","VascularEndothelial","Fibroblast","Fibroblast","CyclingFibroblast","Fibroblast","Chondrocyte","Basal","Schwann/Neural","Fibroblast","Secretory","MesenchymalProgenitor","Stem","Fibroblast","Ciliated","Fibroblast","Chondrocyte","Immune","Muscle","Muscle","LymphaticEndothelial"))
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = Hs_v3_type_sub1, col.name = "specific_type")
table(Hs_GA2123_Trachea_v3@meta.data$specific_type)

      Basal_SE      Basal_SMG       Ciliated Ciliated_Foxn4      Epcam_ECM  Myoepithelial   Secretory_SE  Secretory_SMG 
           308             30            169             66            150             67             54            176 
Hs_GA2123_Trachea_v3@meta.data$specific_type <- ifelse(is.na(Hs_GA2123_Trachea_v3@meta.data$specific_type), as.character(Hs_GA2123_Trachea_v3@meta.data$cell_type), as.character(Hs_GA2123_Trachea_v3@meta.data$specific_type))
now we have annotation for all cells:
table(Hs_GA2123_Trachea_v3@meta.data$specific_type)

             Basal_SE             Basal_SMG           Chondrocyte              Ciliated        Ciliated_Foxn4     CyclingFibroblast 
                  308                    30                   650                   169                    66                   479 
            Epcam_ECM            Fibroblast                Immune  LymphaticEndothelial MesenchymalProgenitor                Muscle 
                  150                  5359                   121                    65                   316                   177 
        Myoepithelial        Schwann/Neural          Secretory_SE         Secretory_SMG                  Stem   VascularEndothelial 
                   67                   405                    54                   176                   286                   815 

print(levels(Hs_GA2123_Trachea_v3@ident))
 [1] "Basal_SE"              "Basal_SMG"             "Chondrocyte"           "Ciliated"              "Ciliated_Foxn4"       
 [6] "CyclingFibroblast"     "Epcam_ECM"             "Fibroblast"            "Immune"                "LymphaticEndothelial" 
[11] "MesenchymalProgenitor" "Muscle"                "Myoepithelial"         "Schwann/Neural"        "Secretory_SE"         
[16] "Secretory_SMG"         "Stem"                  "VascularEndothelial"  
Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
Hs_GA2123_Trachea_v3@ident = factor(Hs_GA2123_Trachea_v3@ident,levels(Hs_GA2123_Trachea_v3@ident)[c(1,15,5,4,2,16,13,7,17,14,9,10,18,12,3,11,8,6)])

DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","MUC5AC","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","SNAP25","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","PI16","CD34","THY1","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T,col.min = -2,col.max = 2)

df_Hs<-FetchData(Hs_GA2123_Trachea_v3,c("ANO1","CFTR","SERPINB3","MUC16","specific_type"))

For the purpose of visualization, we average within each specific cell type:
Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
average_Hs_specific_Annotation<-AverageExpression(object = Hs_GA2123_Trachea_v3,return.seurat = T)
Finished averaging RNA for cluster Basal_SE
Finished averaging RNA for cluster Basal_SMG
Finished averaging RNA for cluster Chondrocyte
Finished averaging RNA for cluster Ciliated
Finished averaging RNA for cluster Ciliated_Foxn4
Finished averaging RNA for cluster CyclingFibroblast
Finished averaging RNA for cluster Epcam_ECM
Finished averaging RNA for cluster Fibroblast
Finished averaging RNA for cluster Immune
Finished averaging RNA for cluster LymphaticEndothelial
Finished averaging RNA for cluster MesenchymalProgenitor
Finished averaging RNA for cluster Muscle
Finished averaging RNA for cluster Myoepithelial
Finished averaging RNA for cluster Schwann/Neural
Finished averaging RNA for cluster Secretory_SE
Finished averaging RNA for cluster Secretory_SMG
Finished averaging RNA for cluster Stem
Finished averaging RNA for cluster VascularEndothelial
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Scaling data matrix

  |                                                                                                                     
  |                                                                                                               |   0%
  |                                                                                                                     
  |===============================================================================================================| 100%

save(Hs_GA2123_Trachea_v3_sub1,file="seurat_GA2123wk_v3_sub1.RData")
save(Hs_GA2123_Trachea_v3,file="seurat_GA2123wk_v3.RData")
load(file="seurat_GA2123wk_v3.RData")
---
title: "Hs_Apr3"
output: html_notebook
---
#### Human fetal trachea samples collected on Apr3. v3 chemistry.
```{r}
library(Seurat)
library(dplyr)
```
```{r}
ZipF<-list.files(path=".",pattern="*.gz",full.names = T,recursive = T)
ZipF
```
```{r}
library(plyr)
library(R.utils)
ldply(.data=ZipF, .fun=gunzip)  #This just unzips locally
```

```{r}
##### First I manually changed all featurres.tsv to genes.tsv. Otherwise Read10X (Seurat v2) would not recognize.
# Load data
file_10Xdir_Hs<-c("GA21wk_v3","GA23wk_v3")
names(file_10Xdir_Hs)<-c("GA21wk_v3","GA23wk_v3")
Hs_Apr3_v3.data <- Read10X(data.dir = file_10Xdir_Hs)
```

```{r}
dim(Hs_Apr3_v3.data)
```
##### 26577 genes for HG38-plus
##### 38892 "cells"/barcodes as filtered by Cell Ranger

```{r}
Hs_GA2123_Trachea_v3 <- CreateSeuratObject(raw.data = Hs_Apr3_v3.data, min.cells = 1, min.genes = 1, 
    project = "Hs_GA2123_Trachea_v3chemistry")
Hs_GA2123_Trachea_v3@raw.data@Dim
```
```{r}
head(Hs_GA2123_Trachea_v3@cell.names)
```

```{r}
Hs_GA2123_Trachea_v3 <- FilterCells(object = Hs_GA2123_Trachea_v3, subset.names = c("nGene","nUMI"), 
    low.thresholds = c(1000,4000), high.thresholds = c(Inf,Inf))
Hs_GA2123_Trachea_v3@data@Dim
```

```{r}
cell_name<-read.table(text=Hs_GA2123_Trachea_v3@cell.names,sep="_",colClasses = "character")
age<-cell_name[,1]
names(age)<-Hs_GA2123_Trachea_v3@cell.names
```
```{r}
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = age, col.name = "age")
```

```{r}
table(Hs_GA2123_Trachea_v3@meta.data$age)
```

```{r}
ribo.genes <- grep(pattern = "^RP[SL][[:digit:]]", x = rownames(x = Hs_GA2123_Trachea_v3@data), value = TRUE)
percent.ribo <- Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data[ribo.genes, ])/Matrix::colSums(Hs_GA2123_Trachea_v3@raw.data)
Hs_GA2123_Trachea_v3 <- AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = percent.ribo, col.name = "percent.ribo")
```

```{r}
aggregate(Hs_GA2123_Trachea_v3@meta.data[, c(1:2,5)], list(Hs_GA2123_Trachea_v3@meta.data$age), median)

```

```{r}
Hs_GA2123_Trachea_v3 <- NormalizeData(object = Hs_GA2123_Trachea_v3)
```

```{r}
Hs_GA2123_Trachea_v3 <- ScaleData(object = Hs_GA2123_Trachea_v3)
```

```{r}
Hs_GA2123_Trachea_v3 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```

```{r}
Hs_GA2123_Trachea_v3 <- RunPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
Hs_GA2123_Trachea_v3 <- ProjectPCA(object = Hs_GA2123_Trachea_v3, do.print = FALSE)
```
```{r,fig.height=50,fig.width=15}
PCHeatmap(object = Hs_GA2123_Trachea_v3, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

```

```{r}
PCElbowPlot(object = Hs_GA2123_Trachea_v3)
```

```{r}
n.pcs = 20
res.used <- 0.8

Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.0.8",pt.size = 0.2)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="age",pt.size = 0.1)
```

```{r}
n.pcs = 20
res.used <- 1.0

Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc=T)
```
```{r}
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1")

```

```{r,fig.height=15,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("ANO1","CFTR","EPCAM","TP63","FOXJ1","FOXN4","SCGB1A1","LTF","SNAP25","ASCL1","CHGA","PLP1","MPZ","SOX10","C1QA","FCER1G","PECAM1","LYVE1","RGS5","NOTCH3","ACTA2","ACTG2","DES","PDLIM3","FGL2","PCDH7","MYH11","COL11A1","SOX9","SOX5","SOX6","COL2A1","ACAN","SERPINF1","COL1A1","THBS2","KERA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","MKI67","TOP2A","TWIST2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1",group.cex = 25,cex.row=25,group.order = c(9,16,12,14,10,19,3,22,20,21,5,7,15,17,8,18,4,0,2,1,13,11,6)
  )
```

```{r}
Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="res.1")
GA2123wk_v3.res1.clust.markers <- FindAllMarkers(object = Hs_GA2123_Trachea_v3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

```

```{r}
GA2123wk_v3.res1.clust.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
```
```{r}
write.table(GA2123wk_v3.res1.clust.markers,"GA2123wk_v3.res1.markers.txt",sep="\t")

```
```{r}
Hs_v3_res1_8_18<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(8),ident.2=c(18),only.pos = F)
Hs_v3_res1_8_18
```

```{r}
Hs_v3_res1_2over1<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(2),ident.2=c(1),only.pos = T)
Hs_v3_res1_2over1
```

```{r}
Hs_v3_res1_21_20<-FindMarkers(Hs_GA2123_Trachea_v3,ident.1=c(21),ident.2=c(20),only.pos = T)
Hs_v3_res1_21_20
```

```{r}
n.pcs = 20
res.used <- 1.2

Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

```{r}
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.2")

```

```{r}
n.pcs = 20
res.used <- 1.4

Hs_GA2123_Trachea_v3 <- FindClusters(object = Hs_GA2123_Trachea_v3, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

```{r}
Hs_GA2123_Trachea_v3 <- RunTSNE(object = Hs_GA2123_Trachea_v3, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = T,group.by="res.1.4")

```

```{r}
load("GA2123wk_apr3_v3_doubletScore.RData")
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = GA2123wk_apr3_v3_doubletScore, col.name = "doublet_score")
sum(is.na(Hs_GA2123_Trachea_v3@meta.data$doublet_score))
```


```{r,fig.height=5, fig.width=20}
Hs_GA2123_Trachea_v3<-SetAllIdent(Hs_GA2123_Trachea_v3,id="age")
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA21wk")
```

```{r,fig.height=5, fig.width=20}
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("doublet_score"), nCol = 1,group.by="res.1.4",point.size.use=0.3,ident.include = "GA23wk")
```

```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("ANO1","CFTR","EPCAM","KRT8","KRT18","TP63","KRT5","KRT14","SOSTDC1","KRT4","KRT13","SPDEF","CREB3L1","MUC5B","FOXJ1","FOXN4","SHISA8","MCIDAS","TUBB3","SNAP25","ASCL1","CHGA","PLP1","MPZ","C1QA","FCER1G","CD3G","PECAM1","NRP1","LYVE1","RGS5","NOTCH3","ACTA2","TAGLN","MYH11","COL8A1","COL11A1","SOX9","COL2A1","ACAN","MIA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","TK1","MKI67","TOP2A","ALAS2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 35,cex.row=25,group.order = c(8,14,17,19,10,21,9,11,24,22,23,3,5,16,18,6,20,13,7,0,1,2,15,12,4)
  )
```


```{r,fig.height=15, fig.width=20}
VlnPlot(object = Hs_GA2123_Trachea_v3, features.plot = c("SOX10","PHOX2A", "PHOX2B","CHGA","ASCL1","RET"), nCol = 1,group.by="res.1.4",point.size.use=0.3)
```
```{r,fig.height=8,fig.width=40}
    DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("EPCAM","TUBB3","SNAP25","ASCL1","CHGA","PHOX2A","PHOX2B","PLP1","MPZ"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.4",group.cex = 35,cex.row=25,cells.use = Hs_GA2123_Trachea_v3@cell.names[Hs_GA2123_Trachea_v3@meta.data$res.1.4 %in% c(10)]
  )
```

##### subset the Non-EPCAM cells:
```{r}
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_nonEpcam<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(0:7,9:13,15,16,18,20:24))
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.1.4)
```

```{r}
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data)[colnames(Hs_GA2123_Trachea_v3_nonEpcam@meta.data) == 'res.1.2'] <- 'orig.1.2'
```

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- ScaleData(object = Hs_GA2123_Trachea_v3_nonEpcam)
```

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_nonEpcam, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```

######run PCA on the set of genes
```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- RunPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_nonEpcam)
```

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- ProjectPCA(object = Hs_GA2123_Trachea_v3_nonEpcam, do.print = F)
```

```{r}
PCElbowPlot(object = Hs_GA2123_Trachea_v3_nonEpcam)
```
```{r,fig.height=30,fig.width=15}
PCHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, pc.use = 1:20, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

```

```{r}
n.pcs = 20
res.used <- 0.8

Hs_GA2123_Trachea_v3_nonEpcam <- FindClusters(object = Hs_GA2123_Trachea_v3_nonEpcam, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE,force.recalc = T)
```

```{r}
Hs_GA2123_Trachea_v3_nonEpcam <- RunTSNE(object = Hs_GA2123_Trachea_v3_nonEpcam, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3_nonEpcam, do.label = T,group.by="res.0.8")

```

```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3_nonEpcam, genes.use = c("ANO1","CFTR","TUBB3","SNAP25","ASCL1","CHGA","PLP1","MPZ","C1QA","FCER1G","CD3G","PECAM1","NRP1","LYVE1","RGS5","NOTCH3","ACTA2","TAGLN","MYH11","COL8A1","COL11A1","SOX9","COL2A1","ACAN","MIA","DCN","LUM","CD34","WNT2","THY1","PI16","CLEC3B","TK1","MKI67","TOP2A","ADIPOQ","CAR3"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.0.8",group.cex = 35,cex.row=25,group.order = c(9,14,2,17,15,16,13,6,8,5,4,0,3,1,10,11,12,7)
  )
```

```{r}
table(Hs_GA2123_Trachea_v3_nonEpcam@meta.data$orig.1.4,Hs_GA2123_Trachea_v3_nonEpcam@meta.data$res.0.8)
```


```{r}
library(ggalluvial)
```
```{r, fig.height=9, fig.width=6}
ggplot(data=Hs_GA2123_Trachea_v3_nonEpcam@meta.data,aes(axis1=orig.1.4,axis2=res.0.8))+geom_alluvium(aes(fill=res.0.8))+geom_stratum(width = 1/12, fill = "black", color = "grey") +geom_label(stat = "stratum", label.strata = TRUE)+scale_x_discrete(limits = c("orig.1.4", "res.0.8"), expand = c(.05, .05))
```

##### Now subset the basal, ciliated, and secretory:
```{r}
Hs_GA2123_Trachea_v3 <- SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "res.1.4")
Hs_GA2123_Trachea_v3_sub1<-SubsetData(object=Hs_GA2123_Trachea_v3,ident.use=c(8,14,19))
table(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.4)
```

```{r}
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.0.8'] <- 'orig.0.8'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.4'] <- 'orig.1.4'
colnames(Hs_GA2123_Trachea_v3_sub1@meta.data)[colnames(Hs_GA2123_Trachea_v3_sub1@meta.data) == 'res.1.2'] <- 'orig.1.2'
```

```{r}
Hs_GA2123_Trachea_v3_sub1 <- ScaleData(object = Hs_GA2123_Trachea_v3_sub1)
```

```{r}
Hs_GA2123_Trachea_v3_sub1 <- FindVariableGenes(object = Hs_GA2123_Trachea_v3_sub1, do.plot = TRUE, x.low.cutoff=0.1,x.high.cutoff = Inf, y.cutoff = 0.5)
```

######run PCA on the set of genes
```{r}
Hs_GA2123_Trachea_v3_sub1 <- RunPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = FALSE)
#PCAPlot(Hs_GA2123_Trachea_v3_sub1)
```

```{r}
Hs_GA2123_Trachea_v3_sub1 <- ProjectPCA(object = Hs_GA2123_Trachea_v3_sub1, do.print = F)
```

```{r}
PCElbowPlot(object = Hs_GA2123_Trachea_v3_sub1)
```
```{r,fig.height=30,fig.width=15}
PCHeatmap(object = Hs_GA2123_Trachea_v3_sub1, pc.use = 1:12, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 25)

```

```{r}
n.pcs = 16
res.used <- 0.8

Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```
```{r}
Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T)

```

```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("TP63","KRT15","KRT5","KRT17","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","KRT4","MUC1","MUC4","MUC20","SERPINB3","GSTP1","ALOX15","CD9","MYH11","ACTG2","MYLK","TAGLN","LTF","AZGP1","DMBT1","FCGBP","CCL28","AQP5","MUC5B","SPDEF","RNASE1","LYZ","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1","TACSTD2"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.0.8",group.cex = 60,cex.row=30,group.order = c(4,2,6,1,7,5,0,3)
  )
```

```{r,fig.height=4,fig.width=16}
DotPlot(object = Hs_GA2123_Trachea_v3_sub1, cols.use = c("forestgreen","magenta3"),genes.plot = c("TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","FOXJ1","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","CFAP53","CETN2","KRT4","KRT13","MUC1","MUC4","MUC16","MUC20","SERPINB3","MYH11","ACTG2","MYLK","APOE","TAGLN","LTF","AZGP1","DMBT1","KCNN4","FCGBP","LRRC26","KRT7","CCL28","AQP5","MUC5B","SPDEF","LYZ","TIMP3","OGN","COL14A1","BGN","MGP","COL11A1","LUM","ACAN","CFTR","ANO1"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

```{r}
prop.table(table(Hs_GA2123_Trachea_v3_sub1@meta.data$age,Hs_GA2123_Trachea_v3_sub1@meta.data$res.0.8),1)

```

```{r}
n.pcs = 16
res.used <- 1.2

Hs_GA2123_Trachea_v3_sub1 <- FindClusters(object = Hs_GA2123_Trachea_v3_sub1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

```{r}
Hs_GA2123_Trachea_v3_sub1 <- RunTSNE(object = Hs_GA2123_Trachea_v3_sub1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30, dim.embed = 2,k.param=10)

```

```{r}
TSNEPlot(object = Hs_GA2123_Trachea_v3_sub1, do.label = T,group.by="res.1.2")

```

```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","SMOC2","SOX9","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","KRT4","MUC1","MUC4","MUC20","SERPINB3","GSTP1","ALOX15","CD9","MYH11","ACTG2","MYLK","TAGLN","LTF","AZGP1","DMBT1","FCGBP","CCL28","AQP5","MUC5B","SPDEF","RNASE1","LYZ","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1","TACSTD2","SERPINB4","SERPINB13","NPPC"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="res.1.2",group.cex = 60,cex.row=30,group.order = c(2,4,6,1,7,8,5,0,3)
  )
```

```{r}
Hs_v3_sub1_res1.2_c8over2_4<-FindMarkers(Hs_GA2123_Trachea_v3_sub1,ident.1=c(8),ident.2 = c(2,4),only.pos = TRUE)
Hs_v3_sub1_res1.2_c8over2_4
```

```{r}
library(plyr)
Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3_sub1@meta.data$res.1.2,from=c("0","1","2","3","4","5","6","7","8"),to=c("Secretory_SMG","Ciliated","Basal_SE","Epcam_ECM","Basal_SE","Myoepithelial","Ciliated_Foxn4","Secretory_SE","Basal_SMG"))
```

```{r,fig.height=30,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3_sub1, genes.use = c("FOXN4","PLK4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","TP63","KRT15","KRT5","KRT17","KRT14","SOSTDC1","SMOC2","NPPC","KRT4","MUC1","MUC4","MUC20","SERPINB3","SERPINB4","SERPINB13","GSTP1","ALOX15","CD9","SOX9","LTF","AQP5","LRRC26","AZGP1","DMBT1","FCGBP","CCL28","MUC5B","SPDEF","RNASE1","LYZ","MYH11","ACTG2","MYLK","TAGLN","TIMP3","OGN","COL14A1","BGN","COL11A1","LUM","ACAN","CFTR","ANO1"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="cell_type",group.cex = 60,cex.row=30,group.order = c("Ciliated_Foxn4","Ciliated","Basal_SE","Secretory_SE","Basal_SMG","Secretory_SMG","Myoepithelial","Epcam_ECM"),
  )
```

##### to annotate Hs_GA2123_Trachea_v3
```{r}
Hs_v3_type_sub1<-Hs_GA2123_Trachea_v3_sub1@meta.data$cell_type
names(Hs_v3_type_sub1)<-Hs_GA2123_Trachea_v3_sub1@cell.names
```

```{r}
Hs_GA2123_Trachea_v3@meta.data$cell_type<-mapvalues(Hs_GA2123_Trachea_v3@meta.data$res.1,from=c("0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20","21","22"),to=c("Fibroblast","Fibroblast","Fibroblast","VascularEndothelial","Fibroblast","Fibroblast","CyclingFibroblast","Fibroblast","Chondrocyte","Basal","Schwann/Neural","Fibroblast","Secretory","MesenchymalProgenitor","Stem","Fibroblast","Ciliated","Fibroblast","Chondrocyte","Immune","Muscle","Muscle","LymphaticEndothelial"))
```

```{r}
Hs_GA2123_Trachea_v3<-AddMetaData(object = Hs_GA2123_Trachea_v3, metadata = Hs_v3_type_sub1, col.name = "specific_type")
```
```{r}
table(Hs_GA2123_Trachea_v3@meta.data$specific_type)
```

```{r}
Hs_GA2123_Trachea_v3@meta.data$specific_type <- ifelse(is.na(Hs_GA2123_Trachea_v3@meta.data$specific_type), as.character(Hs_GA2123_Trachea_v3@meta.data$cell_type), as.character(Hs_GA2123_Trachea_v3@meta.data$specific_type))
```

##### now we have annotation for all cells:
```{r}
table(Hs_GA2123_Trachea_v3@meta.data$specific_type)
```


```{r,fig.height=20,fig.width=60}
DoHeatmap(object = Hs_GA2123_Trachea_v3, genes.use = c("CFTR","ANO1","EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","MUC1","MUC4","MUC20","SERPINB3","CD9","KRT14","SOSTDC1","MUC5B","SPDEF","RNASE1","LYZ","SNAP25","ASCL1","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","ACTA2","RGS5","NOTCH3","SOX9","COL2A1","ACAN","WNT2","THY1","TWIST2","MKI67"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.by="specific_type",group.cex = 30,cex.row=30,group.order = c("Basal_SE","Ciliated_Foxn4","Ciliated","Secretory_SE","Basal_SMG","Secretory_SMG","Myoepithelial","Epcam_ECM","Stem","Schwann/Neural","Immune","VascularEndothelial","LymphaticEndothelial","Muscle","Chondrocyte","MesenchymalProgenitor","Fibroblast","CyclingFibroblast")
  )
```

```{r}
print(levels(Hs_GA2123_Trachea_v3@ident))
```

```{r,fig.height=6,fig.width=12}
Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
Hs_GA2123_Trachea_v3@ident = factor(Hs_GA2123_Trachea_v3@ident,levels(Hs_GA2123_Trachea_v3@ident)[c(1,15,5,4,2,16,13,7,17,14,9,10,18,12,3,11,8,6)])

```

```{r,fig.height=5,fig.width=12}
DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("lightgray","red"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","SNAP25","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","CD34","THY1","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

```{r,fig.height=5,fig.width=14}
DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("CFTR","ANO1","EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","MUC5AC","SPDEF","LTF","LYZ","ACTA2","POU5F1","ESRG","SNAP25","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","MYH11","RGS5","NOTCH3","COL2A1","ACAN","WNT2","PI16","CD34","THY1","TWIST2","MKI67"),group.by = "ident", x.lab.rot = T,plot.legend = T,col.min = -2,col.max = 2)
```

```{r,fig.height=6,fig.width=8}
DotPlot(object = Hs_GA2123_Trachea_v3, cols.use = c("forestgreen","magenta3"),genes.plot = c("FOXJ1","LTF","TP63","WNT2","PI16","CLEC3B","EPCAM","TERC","TERT","CLDN6","POU5F1","LIN28A","ESRG","L1TD1","DPPA4","UTF1","FOXD3-AS1","CRABP1","THY1","TUBB2B","UCHL1","TUBB3","SNAP25","PLP1"),group.by = "ident", x.lab.rot = T,plot.legend = T)
```

```{r}
df_Hs<-FetchData(Hs_GA2123_Trachea_v3,c("ANO1","CFTR","SERPINB3","MUC16","specific_type"))

```

```{r, fig.height=3, fig.width=10}
ggplot(df_Hs,aes(specific_type,CFTR))+geom_dotplot(binaxis="y",aes(fill=specific_type),binwidth=0.05,stackdir="center",position=position_dodge(0.8), dotsize=0.018)+ theme(axis.text.x = element_text(angle = 45,hjust=1))+ stat_summary(aes(color=specific_type),fun.data=mean_sdl, fun.args = list(mult=1), 
                 geom="pointrange",position=position_dodge(0.7))
```

```{r,fig.width=10,fig.height=6}
TSNEPlot(object = Hs_GA2123_Trachea_v3, do.label = F,group.by="specific_type",pt.size = 0.3)+scale_color_manual(values=c('#e6194b' , '#808080','#3cb44b', '#ffe119', '#4363d8', '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#008080', '#e6beff', '#9a6324', '#fabebe',  '#800000', '#aaffc3', '#808000','#ffd8b1', '#000075', '#f58231', '#000000','#fffac8'
))

```

##### For the purpose of visualization, we average within each specific cell type:
```{r}
Hs_GA2123_Trachea_v3<-SetAllIdent(object = Hs_GA2123_Trachea_v3, id = "specific_type")
average_Hs_specific_Annotation<-AverageExpression(object = Hs_GA2123_Trachea_v3,return.seurat = T)
```

```{r,fig.height=15,fig.width=15}

DoHeatmap(object = average_Hs_specific_Annotation, genes.use = c("EPCAM","TP63","KRT5","FOXN4","SHISA8","MCIDAS","SNTN","CDHR3","FOXJ1","MUC16","SERPINB3","SOX9","KRT14","SOSTDC1","MUC5B","MUC5AC","SPDEF","LTF","LYZ","ACTA2","MYH11","POU5F1","ESRG","SNAP25","ASCL1","CHGA","PLP1","MPZ","FCER1G","C1QA","PECAM1","LYVE1","RGS5","NOTCH3","COL2A1","ACAN","WNT2","PI16","CD34","THY1","TWIST2","MKI67","CFTR","ANO1"), 
    slim.col.label = TRUE, group.label.rot = TRUE,use.scaled = T,group.cex = 30,cex.row=20,group.order = c("Basal_SE","Basal_SMG","Ciliated_Foxn4","Ciliated","Secretory_SE",
 "Secretory_SMG", "Myoepithelial","Epcam_ECM","Stem","Schwann/Neural","Immune","LymphaticEndothelial","VascularEndothelial","Muscle","Chondrocyte","MesenchymalProgenitor","Fibroblast","CyclingFibroblast"))
```


```{r}
save(Hs_GA2123_Trachea_v3_sub1,file="seurat_GA2123wk_v3_sub1.RData")
```
```{r}
save(Hs_GA2123_Trachea_v3,file="seurat_GA2123wk_v3.RData")
```
```{r}
load(file="seurat_GA2123wk_v3.RData")
```


































